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> And it would be tedious and pointless to accompany every result with a disclaimer about PAC learning assumptions- "this result is only meaningful if we accept

by srean 8y ago

> And it would be tedious and pointless to accompany every result with a disclaimer about PAC learning assumptions- "this result is only meaningful if we accept that our dataset came from the same distribution as the real world" etc. That assumption is not necessary, as I mentioned and mlthoughts mentions above, we do have tools to deal with discrepancies between test and train distribution. BTW I want talking about research but meat and potatoes applications. I think it is intellectually lazy to raise questions that even the person asking the question and everyone else knows that there cannot be an answer, especially when we dont define things (example 'real world'). Its easy-peasy to ask for things that are epistemologically impossible. On the other hand working within the limitations of impossibility theorems, under assumptions that seems reasonable, to obtain theoretical results as well as practical algorithms to solve real problems -- now that is a valuable proposition. > [1] I'm on the second year of a machine learning PhD Goes without saying. Those who have been through that, can spot our old enlightened newbie self from a mile away :)

YeGoblynQueenne 8y ago
>> I think it is intellectually lazy to raise questions that even the person asking the question and everyone else knows that there cannot be an answer, especially when we dont define things (example 'real world'). Its easy-peasy to ask for things that are epistemologically impossible. On the other hand working within the limitations of impossibility theorems, under assumptions that seems reasonable, to obtain theoretical results as well as practical algorithms to solve real problems -- now that is a valuable proposition. No, I believe that real progress is definitely possible, "epistemologically". That is how science advances- by solving one "impossible" problem after the other. Of course, that's not how the industry rolls. In industry, if you have something that makes money, you sell it, whether it works or not and you don't bother with silly stuff like theoretical justifications and whatnot. See Waymo's driverless cars (with drivers) and Tesla's Autopilot (that can't auto-pilot) etc. As to what is the real world- "everything that isn't in your data set" will do. My earlier comment was just me being gregarious. And this: >> Goes without saying. Those who have been through that, can spot our old enlightened newbie self from a mile away :) -is distasteful. I was having a conversation and you were having a pissing contest. My bad, I guess. No, I'm not a newbie, you are not me but more experienced and I'm probably older than you. Wrong assumptions. Again.
- srean 8y ago
  You missed the keyword -- "enlightened". You might not agree, but what you demonstrate is very common among new PhD students (and I would argue that is a desirable trait). I sure had that in my days of yore. Just clarifying that by 'impossible' I meant a mathematical impossibility theorem, not some notion of "that seems very implausible". Mathematical science does not progress by making impossible statements possible. Rather they work by scoping out and relaxing things (conditions, assumptions and axioms). For example, one does not deal with Russel's paradox by removing the paradox, but by scoping things out (in this case putting restrictions on how you define your sets) so that the paradox does not arise in the restricted, yet practically useful setting. OTOH other hand if someone keeps bringing up 'whoa Russel's paradox, whoa Russel's paradox that's a limitation' (albeit a well known and obvious one) that qualifies as newbie behavior, although an enlightened one. Inference in an arbitrary graphical model is NP hard. It would be lazy to come up with a corner case where a inference algorithm with a short running time will fail. We make progress by accepting that for arbitrary graphs inference is a hard problem and then asking what is a practically relevant subset of graphs where one can indeed give an efficient solution. Pointing out obvious failure cases is exactly what you said -- a [lazy and vapid] pissing contest. It does no one any good, unless the failure case brought up is of a new and an unknown type that provides new insight. If there was any new insight in the cases you brought up I definitely missed them.

by srean 8y ago

YeGoblynQueenne 8y ago

>> I think it is intellectually lazy to raise questions that even the person asking the question and everyone else knows that there cannot be an answer, especially when we dont define things (example 'real world'). Its easy-peasy to ask for things that are epistemologically impossible. On the other hand working within the limitations of impossibility theorems, under assumptions that seems reasonable, to obtain theoretical results as well as practical algorithms to solve real problems -- now that is a valuable proposition. No, I believe that real progress is definitely possible, "epistemologically". That is how science advances- by solving one "impossible" problem after the other. Of course, that's not how the industry rolls. In industry, if you have something that makes money, you sell it, whether it works or not and you don't bother with silly stuff like theoretical justifications and whatnot. See Waymo's driverless cars (with drivers) and Tesla's Autopilot (that can't auto-pilot) etc. As to what is the real world- "everything that isn't in your data set" will do. My earlier comment was just me being gregarious. And this: >> Goes without saying. Those who have been through that, can spot our old enlightened newbie self from a mile away :) -is distasteful. I was having a conversation and you were having a pissing contest. My bad, I guess. No, I'm not a newbie, you are not me but more experienced and I'm probably older than you. Wrong assumptions. Again.

srean 8y ago
You missed the keyword -- "enlightened". You might not agree, but what you demonstrate is very common among new PhD students (and I would argue that is a desirable trait). I sure had that in my days of yore. Just clarifying that by 'impossible' I meant a mathematical impossibility theorem, not some notion of "that seems very implausible". Mathematical science does not progress by making impossible statements possible. Rather they work by scoping out and relaxing things (conditions, assumptions and axioms). For example, one does not deal with Russel's paradox by removing the paradox, but by scoping things out (in this case putting restrictions on how you define your sets) so that the paradox does not arise in the restricted, yet practically useful setting. OTOH other hand if someone keeps bringing up 'whoa Russel's paradox, whoa Russel's paradox that's a limitation' (albeit a well known and obvious one) that qualifies as newbie behavior, although an enlightened one. Inference in an arbitrary graphical model is NP hard. It would be lazy to come up with a corner case where a inference algorithm with a short running time will fail. We make progress by accepting that for arbitrary graphs inference is a hard problem and then asking what is a practically relevant subset of graphs where one can indeed give an efficient solution. Pointing out obvious failure cases is exactly what you said -- a [lazy and vapid] pissing contest. It does no one any good, unless the failure case brought up is of a new and an unknown type that provides new insight. If there was any new insight in the cases you brought up I definitely missed them.